We tried several approaches of selecting a set of statistics as features to extract from data tables. We initially experimented with table embeddings[1]} and other neural feature extraction approaches but their major disadvantage was that these features are not understood or interpretable by end users. Since understanding the various aspects of why a particular visualization is good is relevant for data analysts, for us the interpretability is an essential requirement[2]}. Thus finally we settled on a set of simple and commonly used features that most people working with data have some understanding of. These are listed in REF . Note that these are consistent with the recommendation of focusing on extremes, medians and relationships [3]}. Note that these core statistics can be further enriched as required by specific application settings.
